000 03580nam a22004455i 4500
001 978-3-642-28971-2
003 DE-He213
005 20140220083314.0
007 cr nn 008mamaa
008 120713s2012 gw | s |||| 0|eng d
020 _a9783642289712
_9978-3-642-28971-2
024 7 _a10.1007/978-3-642-28971-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKołodziej, Joanna.
_eauthor.
245 1 0 _aEvolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems
_h[electronic resource] /
_cby Joanna Kołodziej.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aXXVIII, 191 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v419
505 0 _aScheduling Problems in Grid Computing -- Multi-Level Genetic-Based Hierarchical Grid Schedulers -- Security-Driven Scheduling Model for Computational Grid using Multi-level Genetic Meta-heuristics -- Genetic Solutions to Green Scheduling in Computational Grids.
520 _aOne of the most challenging issues in modelling today's large-scale computational systems is to effectively manage highly parametrised distributed environments such as computational grids, clouds, ad hoc networks and P2P networks. Next-generation computational grids must provide a wide range of services and high performance computing infrastructures. Various types of information and data processed in the large-scale dynamic grid environment may be incomplete, imprecise, and fragmented, which complicates the specification of proper evaluation criteria and which affects both the availability of resources and the final collective decisions of users. The complexity of grid architectures and grid management may also contribute towards higher energy consumption. All of these issues necessitate the development of intelligent resource management techniques, which are capable of capturing all of this complexity and optimising meaningful metrics for a wide range of grid applications.   This book covers hot topics in the design, administration and management of dynamic grid environments with a special emphasis on the preferences and autonomous decisions of system users, secure access to the processed data and services, and application of green technologies. It features advanced research related to scalable genetic-based heuristic approaches to grid scheduling, whereby new scheduling criteria, such as system reliability, security, and energy consumption are incorporated into a general scheduling model. This book may be a valuable reference for students, researchers, and practitioners who work on – or who are interested in joining -- interdisciplinary research efforts in the areas of distributed and evolutionary computation.  
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642289705
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v419
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-28971-2
912 _aZDB-2-ENG
999 _c102932
_d102932